b'John Wieting'

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research

∙ 05/23/2023

Evaluating and Modeling Attribution for Cross-Lingual Question Answering

Trustworthy answer content is abundant in many high-resource languages a...

0 Benjamin Müller, et al. ∙

research

∙ 05/19/2023

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages

Data scarcity is a crucial issue for the development of highly multiling...

0 Sebastian Ruder, et al. ∙

research

∙ 03/23/2023

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

To detect the deployment of large language models for malicious use case...

0 Kalpesh Krishna, et al. ∙

research

∙ 03/23/2023

A Gold Standard Dataset for the Reviewer Assignment Problem

Many peer-review venues are either using or looking to use algorithms to...

0 Ivan Stelmakh, et al. ∙

research

∙ 12/21/2022

Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval

Contrastive learning has been successfully used for retrieval of semanti...

0 John Wieting, et al. ∙

research

∙ 10/25/2022

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature

Literary translation is a culturally significant task, but it is bottlen...

0 Katherine Thai, et al. ∙

research

∙ 07/01/2022

QA Is the New KR: Question-Answer Pairs as Knowledge Bases

In this position paper, we propose a new approach to generating a type o...

0 Wenhu Chen, et al. ∙

research

∙ 05/19/2022

RankGen: Improving Text Generation with Large Ranking Models

Given an input sequence (or prefix), modern language models often assign...

0 Kalpesh Krishna, et al. ∙

research

∙ 04/28/2022

Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization

Despite recent advances in abstractive summarization, current summarizat...

0 Yue Dong, et al. ∙

research

∙ 04/10/2022

Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering

Retrieval augmented language models have recently become the standard fo...

0 Wenhu Chen, et al. ∙

research

∙ 10/15/2021

On The Ingredients of an Effective Zero-shot Semantic Parser

Semantic parsers map natural language utterances into meaning representa...

0 Pengcheng Yin, et al. ∙

research

∙ 04/30/2021

Paraphrastic Representations at Scale

We present a system that allows users to train their own state-of-the-ar...

0 John Wieting, et al. ∙

research

∙ 03/11/2021

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

Pipelined NLP systems have largely been superseded by end-to-end neural ...

0 Jonathan H. Clark, et al. ∙

research

∙ 10/24/2020

On Learning Text Style Transfer with Direct Rewards

In most cases, the lack of parallel corpora makes it impossible to direc...

0 Yixin Liu, et al. ∙

research

∙ 10/12/2020

Reformulating Unsupervised Style Transfer as Paraphrase Generation

Modern NLP defines the task of style transfer as modifying the style of ...

0 Kalpesh Krishna, et al. ∙

research

∙ 03/03/2020

Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) is the task of finding referents in a...

0 Shuyan Zhou, et al. ∙

research

∙ 11/10/2019

A Bilingual Generative Transformer for Semantic Sentence Embedding

Semantic sentence embedding models encode natural language sentences int...

0 John Wieting, et al. ∙

research

∙ 09/30/2019

Simple and Effective Paraphrastic Similarity from Parallel Translations

We present a model and methodology for learning paraphrastic sentence em...

0 John Wieting, et al. ∙

research

∙ 09/14/2019

Beyond BLEU: Training Neural Machine Translation with Semantic Similarity

While most neural machine translation (NMT) systems are still trained us...

0 John Wieting, et al. ∙

research

∙ 01/29/2019

No Training Required: Exploring Random Encoders for Sentence Classification

We explore various methods for computing sentence representations from p...

0 John Wieting, et al. ∙

research

∙ 04/17/2018

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

We propose syntactically controlled paraphrase networks (SCPNs) and use ...

0 Mohit Iyyer, et al. ∙

research

∙ 11/15/2017

Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

We extend the work of Wieting et al. (2017), back-translating a large pa...

0 John Wieting, et al. ∙

research

∙ 06/06/2017

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

We consider the problem of learning general-purpose, paraphrastic senten...

0 John Wieting, et al. ∙

research

∙ 04/30/2017

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

We consider the problem of learning general-purpose, paraphrastic senten...

0 John Wieting, et al. ∙

research

∙ 07/10/2016

Charagram: Embedding Words and Sentences via Character n-grams

We present Charagram embeddings, a simple approach for learning characte...

0 John Wieting, et al. ∙

research

∙ 11/25/2015

Towards Universal Paraphrastic Sentence Embeddings

We consider the problem of learning general-purpose, paraphrastic senten...

0 John Wieting, et al. ∙

research

∙ 08/25/2015

Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm

In this paper, we propose a model-based clustering method (TVClust) that...

0 Daniel Khashabi, et al. ∙

research

∙ 12/02/2014

Tiered Clustering to Improve Lexical Entailment

Many tasks in Natural Language Processing involve recognizing lexical en...

0 John Wieting, et al. ∙